 Loan delinquency analysis using predictive modelRiktesh Srivastava, Sachin Kumar Srivastava, Khushboo Agnihotri and Anviti Gupta International Journal of Knowledge and Learning, 2024, vol. 17, issue 6, 615-627 Abstract: The research uses a machine learning approach to appraising the validity of customer aptness for a loan. Banks and non-banking financial companies (NBFC) face significant non-performing assets (NPAs) threats because of the non-payment of loans. In this study, the data is collected from Kaggle and tested using various machine learning models to determine if the borrower can repay its loan. In addition, we analysed the performance of the models [K-nearest neighbours (K-NN), logistic regression, support vector machines (SVM), decision tree, naive Bayes and neural networks]. The purpose is to support decisions that are based not on subjective aspects but objective data analysis. This work aims to analyse how objective factors influence borrowers to default loans, identify the leading causes contributing to a borrower's default loan. The results show that the decision tree classifier gives the best result, with a recall rate of 0.0885 and a false- negative rate of 5.4%. Keywords: non-banking financial companies; NBFC; K-nearest neighbours; K-NN; decision tree; support vector machine; SVM; logistic regression; naïve Bayes; neural network. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijklea:v:17:y:2024:i:6:p:615-627 Access Statistics for this article More articles in International Journal of Knowledge and Learning from Inderscience Enterprises Ltd |
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